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Comparison of Different Sampling Algorithms for Phenotype Prediction

Part of the Lecture Notes in Computer Science book series (LNBI,volume 10814)


In this paper, we compare different sampling algorithms used for identifying the defective pathways in highly underdetermined phenotype prediction problems. The first algorithm (Fisher’s ratio sampler) selects the most discriminatory genes and samples the high discriminatory genetic networks according to a prior probability that it is proportional to their individual Fisher’s ratio. The second one (holdout sampler) is inspired by the bootstrapping procedure used in regression analysis and uses the minimum-scale signatures found in different random hold outs to establish the most frequently sampled genes. The third one is a pure random sampler which randomly builds networks of differentially expressed genes. In all these algorithms, the likelihood of the different networks is established via leave one out cross-validation (LOOCV), and the posterior analysis of the most frequently sampled genes serves to establish the altered biological pathways. These algorithms are compared to the results obtained via Bayesian Networks (BNs). We show the application of these algorithms to a microarray dataset concerning Triple Negative Breast Cancers. This comparison shows that the Random, Fisher’s ratio and Holdout samplers are most effective than BNs, and all provide similar insights about the genetic mechanisms that are involved in this disease. Therefore, it can be concluded that all these samplers are good alternatives to Bayesian Networks which much lower computational demands. Besides this analysis confirms the insight that the altered pathways should be independent of the sampling methodology and the classifier that is used to infer them.


  • Phenotype Prediction
  • Random Holdout
  • Holdout Sample
  • Leave-one-out Cross-validation (LOOCV)
  • Discriminative Genes

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Correspondence to Juan Luis Fernández-Martínez .

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Cernea, A. et al. (2018). Comparison of Different Sampling Algorithms for Phenotype Prediction. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham.

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